NLP分类常用模型（三）：bert 提取特征 + 全连接tf.layers.dense()

悠悠 2023-07-23 09:56 9阅读 0赞

bert提取句向量特征：

from bert_serving.client import BertClient
    # message 这个表示一个batch_size(cporch)数据
        def _get_message_text(self, message):
            all_tokens = []
            # msg 是某一句话
            for msg in message:
                msg_tokens = []
                for t in msg.get("tokens"):
                    text = self._replace_number_blank(t.text)
                    if text != '':
                        # msg_tokens 一句话的所有tokens集合[你，能，不能，查下，余额]
                        msg_tokens.append(text)
                a = str(msg_tokens)
                a = a.replace('[', '')
                a = a.replace(']', '')
                a = a.replace(',', '')
                a = a.replace('\'', '')
                a = a.replace(' ', '')
    
                # 一个batch_size 所有的tokens集合
                all_tokens.append(list(a))
            bert_embedding = BertClient.encode(all_tokens, is_tokenized=True)
            return np.squeeze(bert_embedding)

全连接层结构：

def conv_net(x, n_classes, num_layers, layer_size, C2, dropout, is_training):
        # Define a scope for reusing the variables
        # layer_size == [1024,256]
        # num_layers == 2
        with tf.variable_scope('ConvNet'):
            # Flatten the data to a 1-D vector for the fully connected layer
            x = tf.contrib.layers.flatten(x)
            reg = tf.contrib.layers.l2_regularizer(C2)
            name = 'dense'
            for i in range(num_layers):
                x = tf.layers.dense(inputs=x,
                                    units=layer_size[i],
                                    activation=tf.nn.relu,
                                    kernel_regularizer=reg,
                                    name='hidden_layer_{}_{}'.format(name, i))
                x = tf.layers.dropout(x, rate=dropout, training=is_training)
    
            out = tf.layers.dense(inputs=x,
                                units=n_classes,
                                kernel_regularizer=reg,
                                name='dense_layer_{}'.format(name))
        return out
        
    logits_train = conv_net(self.a_in, num_classes, self.num_hidden_layers, self.hidden_layer_size, self.C2, self.drop_out, is_training=True)
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits_train, labels=self.b_in)
    # get_regularization_loss(): 获取整体的正则化loss。 
    loss = tf.reduce_mean(cross_entropy) + tf.losses.get_regularization_loss()
    self.y_predict = tf.nn.softmax(logits_train)
     train_op = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(loss)